The Genomics Shared Resource provides high-quality and cost-effective next-generation genome, exome, and RNA sequencing services. Instrumentation includes the Illumina HiSeq 2500, Illumina HiSeq 4000, Illumina NextSeq, and Illumina MiSeq.
For queries regarding genome sequencing and data analysis contact email@example.com
RNA-seq: RNA-seq is a nextgen sequencing technique that measures the abundance of RNA transcripts in a sample. It is a powerful tool for understanding dynamics in the transciptome, including gene expression level difference between different physiological conditions or changes that occur during development or over the course of disease progression. Specifically, this application can be used to study phenomena such as gene expression changes, alternative splicing events, allele-specific gene expression, chimeric transcripts, and RNA editing.
Whole genome and whole exome sequencing: Next-generation DNA sequencing makes it possible to rapidly compare the genetic content among samples and identify germline and somatic variants of interest, such as single nucleotide polymorphisms (SNPs), insertions and deletions (indels), copy number variants (CNVs), and other structural variations. Nextgen technologies can quickly generate a sequence of a whole genome, or can be more targeted using an approach called exome sequencing. Exome sequencing focuses specifically on generating reads from known coding regions. In contrast to whole genome sequencing which sequences the entire genome, exome sequencing is a cost-effective approach that can detect single nucleotide or short indel variants in coding regions, and provides sufficient information for many research needs.
Data Analysis: Experienced data analysts in the Genomics Shared Resource specialize in extracting insights from next-generation sequencing and high-throughput screening data. Standard bioinformatics analysis is included in the price of the service: QC, map reads onto reference genomes, estimate of normalized expression level (RPKM/FPKM) of known genes and transcripts, basic comparison of RPKM/FPKM between samples or under different conditions, and summary statistics.
More information can be found at the following links: